Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
User Category Based Estimation of Location Popularity using the Road GPS Trajectory Databases
Shivendra Tiwari, Saroj Kaushik
Pages - 20 - 31     |    Revised - 10-08-2014     |    Published - 15-09-2014
Volume - 4   Issue - 2    |    Publication Date - September 2014  Table of Contents
Trajectory Databases, Trajectory Mining, Popularity Estimation, Region of Interest.
The mining of the user GPS trajectories and identifying the interesting places have been well studied based on the visitor’s frequency. However, every user is given the same importance in the majority of the trajectory mining methods. In reality, the popularity of the place also depends on the category of the visitor i.e. international vs local visitors etc. We are proposing user category based location popularity estimation using the trajectories databases. It includes mainly three steps. First , pre-processing – the error correction and the graph connection establishment in the road network in order to be able to carry the graph based computations. Second , find the stay regions where the travelers spent some time off-the-road. The visitors can be easily categorized for each POI based on the travel distance from the home location. Finally , normalization and popularity estimation – measure the frequency and stay time of the visitors of each category in the places in question. The weighted sum of the frequency and stay time for each category of the visitors is calculated. The final popularity of the places is computed with values of the pre-configured range. We have implemented and evaluated the proposed method using a large real road GPS trajectory of 182 users that was collected in a period of over three years by Microsoft Asia Research group.
CITED BY (2)  
1 Hsieh, H. P.,Li, C. T., & Lin, S. D. (2015). Estimating Potential Customers Anywhere and Anytime Based on Location-Based Social Networks. In Machine Learning and Knowledge Discovery in Databases (pp. 576-592). Springer International Publishing.
2 Hsieh, H. P., Li, C. T., & Lin, S. D. Estimating Potential Customers Anywhere and Anytime on Location-based Social Networks.
1 Google Scholar
2 CiteSeerX
3 refSeek
4 Scribd
5 SlideShare
6 PdfSR
1 S. Tiwari, and S. Kaushik. “Modeling On-the-Spot Learning: Storage, Landmarks Weighting Heuristic and Annotation Algorithm.” In proceedings of CIIA'13 Saida, Algeria, 2013, pp.357-366.
2 S. Tiwari, and S. Kaushik. “Fusion of Navigation Technology and E-Learning Systems for On-the-spot Learning.” In: proceedings of ICWCA’12, Kuala Lumpur, Malaysia. 2012, pp.72-78.
3 G. J. Klir and B. Yuan: “From Classical (crisp) Sets to Fuzzy Sets” in Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall PTR (ECS Professional), 1997.
4 S. Tiwari, and S. Kaushik. “Boundary Points Detection Using Adjacent Grid Block Selection (AGBS) kNN-Join Method.“ In: proceedings of MLDM’12, Berlin, Germany, 2012, pp. 113-127.
5 D. Guoa, S. Liua and H. Jina (2010): “A graph-based approach to vehicle trajectory analysis.“, Journal of Location Based Services, 4:3-4, 183-199
6 R. Dalumpines and D. M. Scott (2011): “GIS-based Map-matching: Development and Demonstration of a Postprocessing Mapmatching Algorithm for Transportation Research.“ In Lecture Notes in Geoinformation and Cartography, 2011, pp 101-120.
7 X. Gu and Q. Zhu. “Fuzzy multi-attribute decision-making method based on eigenvector of fuzzy attribute evaluation space.“ ScienceDirect Decision Support Systems, Volume 41,Issue 2, January 2006, Pages 400–410.
8 “GeoLife GPS Trajectories – Microsoft Research“: http://research.microsoft.com/enus/downloads/b16d359d-d164-469e-9fd4-daa38f2b2e13/default.aspx
9 Y. Zheng, L. Zhang, X. Xie and W. Y. Ma. “Mining interesting locations and travel sequences from GPS trajectories.“ In Proceedings of WWW’09, Madrid Spain, 2009, pp. 791-800.
10 Y. Zheng, Q. Li, Y. Chen, X. Xie, and W. Y. Ma. “Understanding Mobility Based on GPS Data.“ In proceedings of ACM UbiComp’08, Seoul, Korea, 2008, pp. 312-321.
11 Y. Zheng, X. Xie, and W. Y. Ma. “GeoLife: A Collaborative Social Networking Service among User, location and trajectory.“ In IEEE Data Engineering Bulletin 33(2), 2010, pp. 32-40.
12 J. Kang, and H. S. Yong. “Mining Spatio-Temporal Patterns in Trajectory Data.“ In Journal of Information Processing Systems, Vol.6, No.4; December 2010, pp. 521-536.
13 J. Lee, J. Han, X. Li and H. Gonzalez. “TraClass:Trajectory Classification Using Hierarchical Region based and Trajectory based Clustering.“ In VLDB’08, Auckland, NZ, 2008, pp 1081-1094.
14 J. Lee, J. Han, and K.Y. Whang. “Trajectory Clustering: A Partition-and-Group Framework.“,In proceedings of SIGMOD’07, Beijing, China, June 11–14, 2007, pp. 593-604.
15 Z. Yan, S. Spaccapietra. “Towards Semantic Trajectory Data Analysis: A Conceptual and Computational Approach.“, In proceedings of VLDB ’09 Lyon France, August 2009, pp. 24-28.
16 Zenger, B. (2009): “Trajectory based Point of Interest Recommendation.“ In Thesis: School of Computing Science, Simon Fraser University, Canada.
17 OpenStreetMap (2013): http://nominatim.openstreetmap.org/reverse?lat=37.80948&&lon=-122.4773&zoom=3 Last accessed on 14-Jul-2014.
Dr. Shivendra Tiwari
Department of Computer Science and Engineering Indian Institute of Technology Delhi New Delhi, 110016, India - India
Mr. Saroj Kaushik
Department of Computer Science and Engineering Indian Institute of Technology Delhi New Delhi, 110016, India - India